Identification Of Unpaved Roads in a Regional Road Network Using Remote Sensing

Abstract

An accurate inventory of unpaved road network length and condition within a county, state, or region is important for efficient use of resources to manage and maintain this critical transportation asset. Object-based classification techniques provide a cost-effective way to identify unpaved roads within a local agency's road network when the road type (i.e., paved versus unpaved) attribute is missing. We present a Trimble eCognition® algorithm using four band optical aerial imagery and object-based classification to classify roads as paved or unpaved. The ruleset evaluates relationships between bands and allows separation and segmentation of unpaved roads from other pavement classes. The algorithm is applied to unincorporated areas of a six county region in Southeastern Michigan. Tree shadows on roads and the spectral similarity of road construction materials pose challenges to classification accuracy. An accuracy assessment of the classification indicated that the algorithm works well with overall classification accuracy between 82 and 94 percent.